"Two roads diverged in [soft]wood". Targeted dumping, differential pricing methodology, and zeroing: US-Canada anti-dumping in softwood lumber (WTDS534/R)
Bibliographic record
Abstract
The United States and Canada have a long-standing series of disputes over softwood lumber that until now have focused on alleged subsidies and countervailing duties (CVDs). The United States changed things up this time around and the US Department of Commerce (USDOC) found dumping after applying the Differential Pricing Methodology to softwood lumber from Canada. The panel found that the USDOC erroneously aggregated export price differences when applying the DPM, but departed from the WTO Appellate Body’s previous ruling in US – Washing Machines regarding the use of zeroing and the inclusion of differential prices under Article 2.4.2 of the Anti-Dumping Agreement. To date, the United States and Canada have not been able to resolve the long-standing softwood lumber dispute, and this time the focus shifts from subsidies and countervailing duties to anti-dumping duties. It remains to be seen what happens in this specific dispute on appeal—if, and when, the WTO Appellate Body starts to function again. It will also be interesting to see whether this panel decision encourages parties to argue for, and future panels to permit departures from, Appellate Body rulings with which they disagree.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".